In-memory-computing (IMC) SRAM architecture has gained significant attention as it achieves high energy efficiency for computing a convolutional neural network (CNN) model [1]. Recent works investigated the use of analog-mixed-signal (AMS) hardware for high area and energy efficiency [2], [3]. However, AMS hardware output is well known to be susceptible to process, voltage, and temperature (PVT) variations, limiting the computing precision and ultimately the inference accuracy of a CNN. We reconfirmed, through the simulation of a capacitor-based IMC SRAM macro that computes a 256D binary dot product, that the AMS computing hardware has a significant root-mean-square error (RMSE) of 22.5% across the worst-case voltage, temperature (Fig. 16.1.1 top left) and 3-sigma process variations (Fig. 16.1.1 top right). On the other hand, we can implement an IMC SRAM macro using robust digital logic [4], which can virtually eliminate the variability issue (Fig. 16.1.1 top). However, digital circuits require more devices than AMS counterparts (e.g., 28 transistors for a mirror full adder [FA]). As a result, a recent digital IMC SRAM shows a lower area efficiency of 6368F2/b (22nm, 4b/4b weight/activation) [5] than the AMS counterpart (1170F2/b, 65nm, 1b/1b) [3]. In light of this, we aim to adopt approximate arithmetic hardware to improve area and power efficiency and present two digital IMC macros (DIMC) with different levels of approximation (Fig. 16.1.1 bottom left). Also, we propose an approximation-aware training algorithm and a number format to minimize inference accuracy degradation induced by approximate hardware (Fig. 16.1.1 bottom right). We prototyped a 28nm test chip: for a 1b/1b CNN model for CIFAR-10 and across 0.5-to-1.1V supply, the DIMC with double-approximate hardware (DIMC-D) achieves 2569F2/b, 932-2219TOPS/W, 475-20032GOPS, and 86.96% accuracy, while for a 4b/1b CNN model, the DIMC with the single-approximate hardware (DIMC-S) achieves 3814F2/b, 458-990TOPS/W
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29.8 SHARC: Self-Healing Analog with RRAM and CNFETs
Carbon nanotube (CNT) field-effect transistors (CNFETs) are a promising emerging
technology for energy-efficient electronics (Fig. 1). Despite this promise, CNTs are
subject to substantial inherent imperfections; every ensemble of CNTs includes some percentage of metallic CNTs (m-CNTs). m-CNTs result in conductive shorts between CNFET source and drain, resulting in excessive leakage and degraded (potentially incorrect) circuit functionality (Fig. 1). Several techniques have been developed to remove the majority of m-CNTs (no technique today removes 100% of m-CNTs). While these techniques enabled the first digital CNFET circuits, it is still not possible to realize large-scale CNFET analog or mixed-signal CNFET circuits due to m-CNTs. As shown in Fig. 1, while a digital logic gate can still function correctly in the presence of a small fraction of m-CNTs (but with degraded resilience to noise) [1], a single m-CNT in an analog circuit can result in catastrophic failure (e.g., degrading amplifier gain resulting in functional
failure of circuit blocks such as ADCs and DACs)1. This paper presents a circuit design technique, Self-Healing Analog with RRAM and CNFETs (SHARC), that leverages the programmability of non-volatile resistive RAM (RRAM) to automatically “self-heal” analog circuits in the presence of m-CNTs. Using SHARC, we experimentally demonstrate analog CNFET circuits robust to m-CNTs as well as the first mixed-signals CNFET subsystem (4-bit DAC and SAR ADC; these are the largest reported complementary (CMOS) CNFET circuit demonstrations to-date).
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- Award ID(s):
- 1657303
- NSF-PAR ID:
- 10096101
- Date Published:
- Journal Name:
- SHARC: Self-Healing Analog with RRAM and CNFETs
- Page Range / eLocation ID:
- 470 to 472
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISSCC.2019.8662377
